Matches in ScholarlyData for { ?s ?p ?o. }
- 70 sameAs 70.
- 70 abstract "In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches.".
- 70 hasAuthorList authorList.
- 70 isPartOf proceedings.
- 70 keyword "Social Networks and Web 2.0".
- 70 title "Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models".
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- item-1 hasContent wei-chu.
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- item-2 hasContent seung-taek-park.
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- 71 type InProceedings.
- 71 label "Social Search in "Small-World" Experiments".
- 71 sameAs 71.
- 71 abstract "The "algorithmic small-world hypothesis" states that not only are pairs of individuals in a large social network connected by short paths, but that ordinary individuals can find these paths. Although theoretically plausible, empirical evidence for the hypothesis is limited, as most chains in "small-world" experiments fail to complete, thereby biasing estimates of "true" chain lengths. Using data from two recent small-world experiments, comprising a total of 162,328 message chains, and directed at one of 30 "targets" spread across 19 countries, we model heterogeneity in chain attrition rates as a function of individual attributes. We then introduce a rigorous way of estimating true chain lengths that is provably unbiased, and can account for empiricallyobserved variation in attrition rates. Our findings provide mixed support for the algorithmic hypothesis. On the one hand, it appears that roughly half of all chains can be completed in 6-7 steps—thus supporting the "six degrees of separation" assertion—but on the other hand, estimates of the mean are much longer, suggesting that for at least some of the population, the world is not "small" in the algorithmic sense. We conclude that search distances in social networks are fundamentally different from topological distances, for which the mean and median of the shortest path lengths between nodes tend to be similar.".
- 71 hasAuthorList authorList.
- 71 isPartOf proceedings.
- 71 keyword "Social Networks and Web 2.0".
- 71 title "Social Search in "Small-World" Experiments".
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- item-1 hasContent sharad-goel.
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- item-2 hasContent roby-muhamad.
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- item-3 type ListItem.
- item-3 hasContent duncan-watts.
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- 72 type InProceedings.
- 72 label "Behavioral Profiles for Advanced Email Features".
- 72 sameAs 72.
- 72 abstract "We examine the behavioral patterns of email usage in a large-scale enterprise over a three-month period. In particular, we focus on two main questions: (Q1) what do replies depend on? and (Q2) what is the gain of augmenting contacts through the friends of friends from the email social graph? For Q1, we identify and evaluate the significance of several factors that affect the reply probability and the email response time. We find that all factors of our considered set are significant, provide their relative ordering, and identify the recipient list size, and the intensity of email communication between the correspondents as the dominant factors. We highlight various novel threshold behaviors and provide support for existing hypotheses such as that of the least-effort reply. For Q2, we find that the number of new contacts extracted from the friends-of-friends relationships amounts to a large number, but which is still a limited portion of the total enterprise size. We believe that our results provide significant insights towards informed design of advanced email features, including those of social-networking type. Categories & Subject Descriptors: H.4.3 [Communications Applications]: Electronic mail General Terms: Design, Measurement, Human Factors Keywords: Reply time, reply probability, email profiles.".
- 72 hasAuthorList authorList.
- 72 isPartOf proceedings.
- 72 keyword "Social Networks and Web 2.0".
- 72 title "Behavioral Profiles for Advanced Email Features".
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- item-1 type ListItem.
- item-1 hasContent thomas-karagiannis.
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- item-2 hasContent milan-vojnovic.
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- 73 type InProceedings.
- 73 label "A Measurement-driven Analysis of Information Propagation in the Flickr Social Network".
- 73 sameAs 73.
- 73 abstract "Online social networking sites like MySpace, Facebook, and Flickr have become a popular way to share and disseminate content. Their massive popularity has led to viral marketing techniques that at- tempt to spread content, products, and ideas on these sites. How- ever, there is little data publicly available on viral propagation in the real world and few studies have characterized how information spreads over current online social networks. In this paper, we collect and analyze large-scale traces of infor- mation dissemination in the Flickr social network. Our analysis, based on crawls of the favorite markings of 2.5 million users on 11 million photos, aims at answering three key questions: (a) how widely does information propagate in the social network? (b) how quickly does information propagate? and (c) what is the role of word-of-mouth exchanges between friends in the overall propaga- tion of information in the network? Contrary to viral marketing "intuition," we find that (a) even popular photos do not spread widely throughout the network, (b) even popular photos spread slowly through the network, and (c) information exchanged be- tween friends is likely to account for over 50% of all favorite- markings, but with a significant delay at each hop.".
- 73 hasAuthorList authorList.
- 73 isPartOf proceedings.
- 73 keyword "Social Networks and Web 2.0".
- 73 title "A Measurement-driven Analysis of Information Propagation in the Flickr Social Network".
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- item-1 hasContent meeyoung-cha.
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- item-2 hasContent alan-mislove.
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- item-3 type ListItem.
- item-3 hasContent krishna-p-gummadi.
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- 74 type InProceedings.
- 74 label "Network Analysis of Collaboration Structure in Wikipedia".
- 74 sameAs 74.
- 74 abstract "In this paper we give models and algorithms to describe and analyze the collaboration among authors of Wikipedia from a network analytical perspective. The edit network encodes who interacts how with whom when editing an article; it significantly extends previous network models that code author communities in Wikipedia. Several characteristics summarizing some aspects of the organization process and allowing the analyst to identify certain types of authors can be obtained from the edit network. Moreover, we propose several indicators characterizing the global network structure and methods to visualize edit networks. It is shown that the structural network indicators are correlated with quality labels of the associated Wikipedia articles.".
- 74 hasAuthorList authorList.
- 74 isPartOf proceedings.
- 74 keyword "Social Networks and Web 2.0".
- 74 title "Network Analysis of Collaboration Structure in Wikipedia".
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- authorList hasItem item-1.
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